Using commercial claims for 2012-2013 from Colorado’s All-Payer Claims Database, we examine how medical service prices vary for five hospital-based procedures and the complexity adjusted inpatient price. We find that prices vary substantially in multiple dimensions. Our analysis indicates that there is significant price variation across payers for the same service in the same hospital. If prices converged to the lowest rate each hospital receives, commercial expenditures would fall by 10-20%. The share of overall price variation accounted for by hospitals variation tends to be even more substantial. For four out of six prices, we find that differences associated just with hospitals’ metropolitan areas account for over 45% of the total variation. We observe substantial residual variation (17-50%) after accounting for factors specific to a given payer or provider.

Predictive analytics and “big data” are emerging as important new tools for diagnosing and treating patients. But as data collection becomes more pervasive, and as machine learning and analytical methods become more sophisticated, the companies that traffic in health-related big data will face competitive pressures to make more aggressive claims regarding what their programs can predict. Already, patients, practitioners, and payors are inundated with claims that software programs, “apps,” and other forms of predictive analytics can help solve some of the health care system’s most pressing problems. This article considers the evidence and substantiation that we should require of these claims, focusing on “health” claims, or claims to diagnose, treat, or manage diseases or other medical conditions. The problem is that three very different paradigms might apply, depending on whether we cast predictive analytics as akin to medical products, medical practice, or merely as medical information. Because big data methods are so opaque, its claims may be uniquely difficult to substantiate, requiring a new paradigm. This article offers a new framework that considers intended users and appropriate evidentiary baselines.

This article examines the possible constructs behind the announcement by Amazon, Berkshire Hathaway, and JPMorgan Chase & Co., that they are jointly building a new healthcare entity for their employees. The article provides context by discussing and comparing the healthcare ambitions of the three largest information technology companies and argues that various forms of hybrid entities will increase their footprint in healthcare data and delivery. The core of the article is a thought experiment about the nature of what the article terms “Prime Health.” That analysis is based initially on observations about Amazon’s existing culture and business model of Amazon. Thereafter the article examines both what Prime Health could and should be, arguing that it will go beyond the pedestrian model of a very large self-funded group insurance plan, will disintermediate traditional healthcare insurers, and attempt to bring consumers and healthcare providers together into some type of online marketplace; an updated, privatized version of managed competition. The final parts of the article deal with the regulatory environment that hybrid healthcare generally and Prime Health in particular will face. The analysis includes federal device and data protection laws, some idiosyncratic state laws, and a brief discussion of the problems inherent in the limited regulation of hybrid healthcare entities.

Should the government subsidize health insurance, health care facilities, or both? The United States has subsidized both for many decades, targeting under-served populations and geographic areas. We study these questions in the first rigorous quantitative analysis of two major natural experiments in Appalachian coal country. In the early 1950s, the United Mine Workers of America (UMWA) coal mining union began to provide free health insurance to coal miners and their families. A few years later, the UWMA opened ten new state-of-the-art hospitals in Appalachia. These interventions give us the unique opportunity to separately identify (i) the effect of health insurance from (ii) the combined effect of the insurance plus new hospitals for the same place, time, and population. To do so, we use difference-in-differences at the county-year level. We find that the health insurance had large effects on pregnant women and infants. A woman’s probability of delivering her baby in a hospital increased from 60 percent to over 90 percent. The probability of her infant dying before the age of one decreased from 36 to 9 per 1,000. For the new hospitals, crowd-out was low. Adding UMWA hospitals increased hospital beds by more than 50 percent. Health care workers more than doubled.

The kidney voucher program was started by the National Kidney Registry (NKR) in December 2014. Under the program, live donors can donate a kidney in return for a voucher that entitles their intended recipients to a live donor kidney from the end of a future kidney chain. The purpose of the program is twofold. First, it allows live donors to donate when it is convenient or possible for them to do so. For example, the first voucher donor was 64 years old and had a four-year-old grandson with one poorly functioning kidney. Under the kidney voucher program, the grandfather could donate his kidney before he was too old to donate and provide an advantage for his grandson, who was not expected to need a kidney for ten to fifteen years. Second, voucher donors will help alleviate the shortage of live donors needed to start kidney chains.

To make the kidney voucher program a success, live donors must trust that their intended recipients will receive kidneys when they need them. Viewing the voucher as an enforceable contract would help engender this trust. This article addresses the major legal and policy issues related to whether vouchers should be considered enforceable contracts. The article first confronts whether kidney vouchers violate the proscription in the National Organ Transplant Act (NOTA) against trading organs for valuable consideration. The article demonstrates that, with the current safeguards in place, the legislative history of NOTA supports the legality of trading a live donor kidney for a voucher that entitles the intended recipient to an end-of-chain kidney. The article also tackles the issues of whether a voucher agreement can be considered binding given that the live kidney donor cannot be compelled to donate even if he or she agrees to do so and whether the voucher agreement is illusory because the NKR does not guarantee that the intended voucher recipient will ever receive a kidney. The article concludes that the voucher agreement is binding, discusses the policy implications of reaching this conclusion, and makes some suggestions to deal with the policy concerns.

In 2010, the social networking site Facebook launched a platform allowing private companies to request users’ permission to access personal data. Few users were aware of the platform, which was integrated into Facebook’s terms of service. In 2014, Cambridge Analytica, a UK-based political consulting firm, developed a data-harvesting app. That app prompted Facebook users to provide psychological profiles, including responses such as “I get upset easily” and “I have frequent mood-swings” as part of a “research project.”
The Facebook platform allowed users to share their friends’ data as well, enabling Cambridge Analytica to access tens of millions of personal profiles, identifying voters’ political preferences. The controversy revealed risks to identifiable health data posed by social media and web services companies’ practices. After the Cambridge Analytica controversy, Facebook suspended a project that aimed to link data about users’ medical conditions with information about their social networks.
Individuals often reveal detailed, sensitive health information online. Through wearable devices, social media posts, traceable web searches, and online patient communities, users generate large volumes of health data. Although some individuals participate in online patient forums and wellness information sharing apps under their own names, others participate via pseudonyms, assuming their privacy is preserved. Many users believe their data will be shared only with those they designate.

Existing research on the economic impacts of regulation largely focuses on federal or cross-country regulatory restrictions, but the problem of regulatory accumulation is expected to also occur at the state level. Public choice economics and market process theory offer insight into why regulations alter economic outcomes. Since regulations change the rules of the game and the payoffs that participants receive, looking beyond stated intentions to the way regulations motivate behaviors is critical. Markets are an entrepreneurially driven process characterized by changing conditions, but regulations can inhibit creative destruction and distort incentives. I use the novel State RegData dataset from the QuantGov platform, which analyzes state regulatory texts to provide measures of restriction counts and industry relevance. I estimate the effect of industry-relevant restrictions on business establishments and employment using two econometric models: a multivariate linear regression model with controls and a fixed-effects regression model. I find tentative results that a greater amount of regulation in states is associated with negative percent changes in establishments and employment. My study is a starting point for future investigations of the relationship between regulation and state-level economic outcomes.